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https://issues.apache.org/jira/browse/PIG-4601?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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liyunzhang_intel updated PIG-4601:
----------------------------------
Comment: was deleted
(was: explain more about why there are difference when loading multiple splits
in spark and mr. In MR, org.apache.hadoop.mapreduce.JobSubmitter#writeNewSplits
will sort the splits into order based on size while in spark this will not
happen.
{code}
writeNewSplits () {
List<InputSplit> splits = input.getSplits(job);
//...
T[] array = (T[]) splits.toArray(new InputSplit[splits.size()]);
// sort the splits into order based on size, so that the biggest
// go first
Arrays.sort(array, new SplitComparator());
JobSplitWriter.createSplitFiles(jobSubmitDir, conf,
jobSubmitDir.getFileSystem(conf), array);
}
{code})
> Implement Merge CoGroup for Spark engine
> ----------------------------------------
>
> Key: PIG-4601
> URL: https://issues.apache.org/jira/browse/PIG-4601
> Project: Pig
> Issue Type: Sub-task
> Components: spark
> Affects Versions: spark-branch
> Reporter: Mohit Sabharwal
> Assignee: liyunzhang_intel
> Fix For: spark-branch
>
> Attachments: PIG-4601_1.patch, PIG-4601_2.patch, PIG-4601_3.patch,
> PIG-4601_4.patch
>
>
> When doing a cogroup operation, we need do a map-reduce. The target of merge
> cogroup is implementing cogroup only by a single stage(map). But we need to
> guarantee the input data are sorted.
> There is performance improvement for cases when A(big dataset) merge cogroup
> B( small dataset) because we first generate an index file of A then loading A
> according to the index file and B into memory to do cogroup. The performance
> improves because there is no cost of reduce period comparing cogroup.
> How to use
> {code}
> C = cogroup A by c1, B by c1 using 'merge';
> {code}
> Here A and B is sorted.
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